1. Field of the Invention
The present invention relates to a prognostics and health management (PHM) method for natural aging systems. More specifically, the present invention relates to a prognostic method for detecting anomalies in a system and determining whether the detected anomalies are due to natural aging or other aging processes which can be precursors to failure.
2. Review of the Related Art
Natural aging is a process in which the properties or attributes (such as shape; dimension; weight; condition indicators; functional indicators; performance; or mechanical, chemical, or electrical properties) of a material, structure, or system gradually change (for better or worse) over time or with use. Natural aging can be divided into negative aging and positive aging. Negative aging is often manifested as degradation, such as a reduction in diameter from wear, loss in material strength from fatigue or thermal aging, a loss of dielectric strength, the cracking of insulation, a shift in electrical parameters, etc. Negative aging increases the failure rate of a system and is often accelerated by adverse environmental and operational conditions. Negative aging can lead to the failure of the system if the effects of aging accumulate to a certain critical level.
Positive aging manifests itself in the form of changes that improve the properties or attributes of a system. For example, the increase in concrete strength from curing, reduced vibration from wear-in of rotating machinery, etc. There may also be “other aging” that differs from the main population of natural aging systems due to flaws or defects in the material structures or systems.
PHM is an enabling discipline of technologies and methods that permit the reliability of a system to be evaluated in actual life cycle conditions in order to determine the advent of a failure and mitigate system risks. PHM combines the sensing and interpretation of environmental, operational, and performance-related parameters to indicate the health of a system. PHM can provide advanced warning of failures; can reduce the life cycle cost of a system by decreasing inspection costs, downtime, and inventory; and can assist in the design and logistical support of fielded and future systems.
PHM methods can be classified as data-driven methods, physics-of-failure (PoF) methods, and fusion methods that combine data-driven methods and PoF methods. Data-driven prognostic methods can use available and historical information to statistically and probabilistically derive decisions, estimates, and predictions about the health and reliability of systems. The data-driven methods include statistical methods and machine learning methods. The PoF approach uses underlying engineering and failure principles to model and predict remaining useful life. PoF methods require models based on an understanding of the physics of the failure or failure mechanisms
Previous research on aging of a system has focused on aging risk evaluation and assessment. Failure-rate-based models and physical probabilistic analysis models have been presented in the literature. Failure-rate-based models are often created based on a distribution such as Weibull, or an exponential distribution. But these models require adequate data to statistically analyze the change of a failure rate. Physical probabilistic analysis investigates the possible aging mechanisms, including fatigue, corrosion, and radiation, etc. However, there are many difficulties regarding the use of these models in actual applications. First, the degradation of a system is often caused by a combination of different processes, including aging. If the aging models are used to evaluate degradation due to factors not only including aging, the models will provide inaccurate results. Second, current methods lack a way to identify aging: in fact they identify aging by the observation of aging effects based on experience with the specific equipment in a similar application. Third, the aging mechanisms can be complex when the system is operated under multiple stresses. The models created based on a single stress are not suitable to a system operating under multiple stresses.
Conventional data driven prognostic methods for natural aging systems have problems when analyzing natural aging data. First, the conventional training process of some data-driven methods, such as the multivariate state estimation technique (MSET), the Mahalanobis distance (MD), and Neural Network (NN), cannot meet the requirements of the detection of natural aging systems if sufficient historical training data is not available. During the conventional training process, if historical data is not available, the training data is often obtained from the early, healthy stage of the detected system itself and is fixed once it is selected. As this early established fixed training data baseline cannot contain the entire range of normal, (i.e. healthy) data for a naturally aging system, the data-driven method can generate a false alarm, even when a new, normal pattern occurs. The second problem is that the data-driven methods cannot determine whether the detected anomalies are due to other aging processes within a naturally aging system.